[sahithya]

Month 1-2: Fundamentals

Week 1-2: Introduction to Machine Learning and Python

Learn the basics of Python programming.
Understand fundamental concepts in machine learning.

Week 3-4: Linear Algebra and Statistics

Brush up on linear algebra and statistics, which are crucial for understanding machine learning algorithms.

Month 3-4: Supervised Learning

Week 1-2: Regression

Study linear and non-linear regression techniques.

Week 3-4: Classification

Explore binary and multi-class classification methods.

Month 5-6: Unsupervised Learning

Week 1-2: Clustering

Learn about K-Means, hierarchical clustering, and DBSCAN.

Week 3-4: Dimensionality Reduction

Study techniques like PCA and t-SNE.

Month 7-8: Deep Learning

Week 1-2: Neural Networks

Understand the basics of feedforward neural networks.

Week 3-4: Convolutional Neural Networks (CNNs)

Dive into image processing and CNNs.

Month 9-10: Deep Learning Continued

Week 1-2: Recurrent Neural Networks (RNNs)

Explore sequential data and RNNs.

Week 3-4: GANs and Transfer Learning

Study Generative Adversarial Networks and transfer learning with pre-trained models.

Month 11-12: Specialized Topics

Week 1-2: Natural Language Processing (NLP)

Delve into NLP, including text classification, sentiment analysis, and language models.

Week 3-4: Reinforcement Learning

Understand the basics of reinforcement learning and Q-learning.